PyTorch深度学习快速入门(小土堆)

16. 神经网络的基本骨架

forward:

import torch
from torch import nn

class Tudui(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self,input):
        output=input+1
        return output

#创建Tudui的实例对象
tudui=Tudui()
#创建一个输入张量x
x=torch.tensor(1.0)
#将输入张量传递给tudui模型的foward()
output=tudui(x)
print(output)

输入: x
卷积
非线性
卷积
非线性
输出

17.卷积操作

import torch
import torch.nn.functional as F

input=torch.tensor([[1,2,0,3,1],
                   [0,1,2,3,1],
                   [1,2,1,0,0],
                   [5,2,3,1,1],
                   [2,1,0,1,1]])

kernel=torch.tensor([[1,2,1],
                     [0,1,0],
                     [2,1,0]])

input=torch.reshape(input,(1,1,5,5))
kernel=torch.reshape(kernel,(1,1,3,3))

print(input.shape)
print(kernel.shape)

#卷积操作
#stride:移动步长
output=F.conv2d(input,kernel,stride=1)
print(output)

output2=F.conv2d(input,kernel,stride=2)
print(output2)

#padding:对输入张量四周进行填充
output3=F.conv2d(input,kernel,stride=1,padding=1)
print(output3)

18.卷积层

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset=torchvision.datasets.CIFAR10("data1",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader=DataLoader(dataset,batch_size=64)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.conv1=Conv2d(in_channels=3,out_channels=6,kernel_size=3,stride=1,padding=0)

    def forward(self,x):
        x=self.conv1(x)
        return x

tudui=Tudui()

writer=SummaryWriter("logs1")

step=0
for data in dataloader:
    imgs,targets=data
    output=tudui(imgs)
    print(imgs.shape)
    print(output.shape)

    writer.add_images("input",imgs,step)

    output=torch.reshape(output,(-1,3,30,30))
    writer.add_images("output",output,step)
    step=step+1

19. 最大池化的使用

池化层的步长默认是卷积核的大小.

最大池化:提取特征,剔除冗余(减少数据量,并降低维度)

import torch
from torch import nn
from torch.nn import MaxPool2d

input=torch.tensor([[1,2,0,3,1],
                    [0,1,2,3,1],
                    [1,2,1,0,0],
                    [5,2,3,1,1],
                    [2,1,0,1,1]])

input=torch.reshape(input,(-1,1,5,5))
print(input.shape)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.maxpool=MaxPool2d(kernel_size=3,ceil_mode=True)

    def forward(self,input):
        output=self.maxpool(input)
        return output

tudui=Tudui()
output=tudui(input)
print(output)

import torch
import torchvision.datasets
from torch import nn
from torch.nn import MaxPool2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset=torchvision.datasets.CIFAR10("data1",train=False,download=True,transform=torchvision.transforms.ToTensor())

dataloader=DataLoader(dataset,batch_size=64)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.maxpool=MaxPool2d(kernel_size=3,ceil_mode=True)

    def forward(self,input):
        output=self.maxpool(input)
        return output

tudui=Tudui()

writer=SummaryWriter("logs_maxpool")
step=0

for data1 in dataloader:
    imgs,targets=data1
    writer.add_images("input",imgs,step)
    output=tudui(imgs)
    writer.add_images("output",output,step)
    step=step+1

writer.close()

20. 非线性激活

import torch
from torch import nn
from torch.nn import ReLU

input=torch.tensor([[1,-0.5],
                    [-1,3]])

input=torch.reshape(input,(-1,1,2,2))
print(input.shape)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.relu1=ReLU()

    def forward(self,input):
        output=self.relu1(input)
        return output
    
tudui=Tudui()
output=tudui(input)
print(output)

sigmoid():压缩图片灰度

import torch
import torchvision.datasets
from torch import nn
from torch.nn import ReLU, Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

input=torch.tensor([[1,-0.5],